03. Calculations

Chris Bailiss

2023-10-01

Source: vignettes/v03-calculations.Rmd

In This Vignette

  • Calculations and Calculation Groups
  • Calculation Types
  • Basic Calculation Methods
    • Method 1: Summarising Values
    • Method 2: Deriving values from other summarised values
  • Moving the calculations in the pivot table
    • Displaying calculations on rows
    • Specifying calculations before row/column data groups
  • Filtering data as part of a calculation
  • % of Totals, Cumulative Sums, Running Differences, Rolling Averages, Ratios/Multiples
  • Formatting calculated values
  • Empty cells
  • Using multiple data frames in the same pivot table
  • Advanced Calculation Methods
    • Method 3: Custom calculation functions
    • Method 4: Showing a value (no calculation)
  • Performance considerations
  • Further Reading

Calculations and Calculation Groups

Calculations define how (typically numerical) data is to be summarised/aggregated. Common ways of summarising data include sum, avg (mean, median, …), max, min, etc.

Within a pivottabler pivot table, calculations always belong to a Calculation Group. Calculation groups allow calculations to be defined that refer to other calculations.

Every pivot table always has a default calculation group (called default). This is sufficient for most scenarios. All the calculations defined in this vignette sit in the default calculation group.

Creating additional calculation groups is only necessary for some advanced pivot table layouts - see the Irregular Layout vignette for an example.

Calculations groups are not discussed further in this vignette.

Calculation Types

The pivottabler package supports several different ways of calculating the values to display in the cells of the pivot table:

  1. Summarise values (dplyr summarise or data.table calculate expression)
  2. Deriving values from other summarised values
  3. Custom calculation functions
  4. Show a value (no calculation)

Calculations are added to the pivot table using the defineCalculation() function.

The first two of the methods listed above are described in more detail in the following section. The third and fourth methods are less commonly needed and are described at the end of this vignette.

Basic Calculation Methods

Calculation Method 1: Summarising Values

The most common way to calculate the pivot table is to provide an expression that describes how to aggregate the data, e.g. defining a calculation that counts the number of trains:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$renderPivot()

The pivottabler package uses the dplyr package by default. The summariseExpression is therefore an expression that can be used with the dplyr summarise() function. The following shows several different example expressions:

library(pivottabler)
library(dplyr)
library(lubridate)

# derive some additional data
trains <- mutate(bhmtrains,
   ArrivalDelta=difftime(ActualArrival, GbttArrival, units="mins"),
   ArrivalDelay=ifelse(ArrivalDelta<0, 0, ArrivalDelta))

# create the pivot table
pt <- PivotTable$new()
pt$addData(trains)
pt$addRowDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="TotalTrains", caption="Total Trains", 
                     summariseExpression="n()")
pt$defineCalculation(calculationName="MinArrivalDelay", caption="Min Arr. Delay", 
                     summariseExpression="min(ArrivalDelay, na.rm=TRUE)")
pt$defineCalculation(calculationName="MaxArrivalDelay", caption="Max Arr. Delay", 
                     summariseExpression="max(ArrivalDelay, na.rm=TRUE)")
pt$defineCalculation(calculationName="MeanArrivalDelay", caption="Mean Arr. Delay", 
                     summariseExpression="mean(ArrivalDelay, na.rm=TRUE)", format="%.1f")
pt$defineCalculation(calculationName="MedianArrivalDelay", caption="Median Arr. Delay", 
                     summariseExpression="median(ArrivalDelay, na.rm=TRUE)")
pt$defineCalculation(calculationName="IQRArrivalDelay", caption="Delay IQR", 
                     summariseExpression="IQR(ArrivalDelay, na.rm=TRUE)")
pt$defineCalculation(calculationName="SDArrivalDelay", caption="Delay Std. Dev.", 
                     summariseExpression="sd(ArrivalDelay, na.rm=TRUE)", format="%.1f")
pt$renderPivot()

The data.table package can also be used - see the Performance vignette for details. Note that the “count” summarise expression is specified as .N when using data.table, not as n().

When using data.table, be aware of some strange behaviour that leads to incorrect values in the pivot table when aggregating over columns that are also used in either row/column headings. Again, see the Performance vignette for details.

Calculation Method 2: Deriving values from other summarised values

Calculations can be defined that refer to other calculations, by following these steps:

  1. Specifying type="calculation",
  2. Specifying the names of the calculations which this calculation is based on in the basedOn argument.
  3. Specifying an expression for this calculation in the calculationExpression argument. The values of the base calculations are accessed as elements of the values list.

For example, calculating the percentage of trains with an arrival delay of greater than five minutes:

library(pivottabler)
library(dplyr)
library(lubridate)

# derive some additional data
trains <- mutate(bhmtrains,
   ArrivalDelta=difftime(ActualArrival, GbttArrival, units="mins"),
   ArrivalDelay=ifelse(ArrivalDelta<0, 0, ArrivalDelta),
   DelayedByMoreThan5Minutes=ifelse(ArrivalDelay>5,1,0))

# create the pivot table
pt <- PivotTable$new()
pt$addData(trains)
pt$addRowDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="DelayedTrains", caption="Trains Arr. 5+ Mins Late", 
                     summariseExpression="sum(DelayedByMoreThan5Minutes, na.rm=TRUE)")
pt$defineCalculation(calculationName="TotalTrains", caption="Total Trains", 
                     summariseExpression="n()")
pt$defineCalculation(calculationName="DelayedPercent", caption="% Trains Arr. 5+ Mins Late", 
                     type="calculation", basedOn=c("DelayedTrains", "TotalTrains"), 
                     format="%.1f %%",
                     calculationExpression="values$DelayedTrains/values$TotalTrains*100")
pt$renderPivot()

The base calculations can be hidden by specifying visible=FALSE, e.g. to look at how the percentage of trains more than five minutes late varied by month and train operating company:

library(pivottabler)
library(dplyr)
library(lubridate)

# derive some additional data
trains <- mutate(bhmtrains,
   GbttDateTime=if_else(is.na(GbttArrival), GbttDeparture, GbttArrival),
   GbttMonth=make_date(year=year(GbttDateTime), month=month(GbttDateTime), day=1),
   ArrivalDelta=difftime(ActualArrival, GbttArrival, units="mins"),
   ArrivalDelay=ifelse(ArrivalDelta<0, 0, ArrivalDelta),
   DelayedByMoreThan5Minutes=ifelse(ArrivalDelay>5,1,0))

# create the pivot table
pt <- PivotTable$new()
pt$addData(trains)
pt$addColumnDataGroups("GbttMonth", dataFormat=list(format="%B %Y"))
pt$addRowDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="DelayedTrains", visible=FALSE,
                     summariseExpression="sum(DelayedByMoreThan5Minutes, na.rm=TRUE)")
pt$defineCalculation(calculationName="TotalTrains", visible=FALSE,
                     summariseExpression="n()")
pt$defineCalculation(calculationName="DelayedPercent", caption="% Trains Arr. 5+ Mins Late", 
                     type="calculation", basedOn=c("DelayedTrains", "TotalTrains"), 
                     format="%.1f %%",
                     calculationExpression="values$DelayedTrains/values$TotalTrains*100")
pt$renderPivot()

Moving the calculations in the pivot table

By default the calculation headings (if visible) are placed in the pivot table as column headings, underneath any column data groups.

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="NumberOfTrains", caption="Number of Trains",
                     summariseExpression="n()")
pt$defineCalculation(calculationName="MaximumSpeedMPH", caption="Maximum Speed (MPH)",
                     summariseExpression="max(SchedSpeedMPH, na.rm=TRUE)")
pt$renderPivot()

The location of these headings can be moved as illustrated below.

Displaying calculations on rows

Calculations can be swapped onto the rows using the addRowCalculationGroups() method:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="NumberOfTrains", caption="Number of Trains",
                     summariseExpression="n()")
pt$defineCalculation(calculationName="MaximumSpeedMPH", caption="Maximum Speed (MPH)",
                     summariseExpression="max(SchedSpeedMPH, na.rm=TRUE)")
pt$addRowCalculationGroups()
pt$renderPivot()

Specifying calculations before row/column data groups

In the examples above, the row/column groups were specified first and then the calculations. It is equally possible to specify the calculations first. The calculation names then form the first level of the row/column groups, e.g.

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$defineCalculation(calculationName="NumberOfTrains", caption="Number of Trains",
                     summariseExpression="n()")
pt$defineCalculation(calculationName="MaximumSpeedMPH", caption="Maximum Speed (MPH)",
                     summariseExpression="max(SchedSpeedMPH, na.rm=TRUE)")
pt$addColumnCalculationGroups()
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$renderPivot()

Similarly, on rows:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$defineCalculation(calculationName="NumberOfTrains", caption="Number of Trains",
                     summariseExpression="n()")
pt$defineCalculation(calculationName="MaximumSpeedMPH", caption="Maximum Speed (MPH)",
                     summariseExpression="max(SchedSpeedMPH, na.rm=TRUE)")
pt$addRowCalculationGroups()
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$renderPivot()

Again on rows, but this time using outline layout:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$defineCalculation(calculationName="NumberOfTrains", caption="Number of Trains",
                     summariseExpression="n()")
pt$defineCalculation(calculationName="MaximumSpeedMPH", caption="Maximum Speed (MPH)",
                     summariseExpression="max(SchedSpeedMPH, na.rm=TRUE)")
pt$addRowCalculationGroups(outlineBefore=TRUE)
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$renderPivot()

See the Regular Layout vignette for more details on outline layout.

Filtering data as part of a calculation

It is possible to specify additional/different filter criteria as part of a calculation definition. This additional criteria can either be in the form of a PivotFilters object or a PivotFiltersOverrides object.

For example, to calculate the percentage of trains of each category that each train company operated at weekends, for each cell in the pivot table:

  1. Additionally filter the data to trains operating at weekends and count the number of trains at weekends,
  2. Count the total number of trains (with no additional filter, i.e. irrespective of weekday or weekend),
  3. Calculate the percentage.
library(dplyr)
library(lubridate)
library(pivottabler)

# get the date of each train and whether that date is a weekday or weekend
trains <- bhmtrains %>%
  mutate(GbttDateTime=if_else(is.na(GbttArrival), GbttDeparture, GbttArrival),
         DayNumber=wday(GbttDateTime),
         WeekdayOrWeekend=ifelse(DayNumber %in% c(1,7), "Weekend", "Weekday"))

# render the pivot table
pt <- PivotTable$new()
pt$addData(trains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
weekendFilter <- PivotFilters$new(pt, variableName="WeekdayOrWeekend", values="Weekend")
pt$defineCalculation(calculationName="WeekendTrains", summariseExpression="n()", 
                     filters=weekendFilter, visible=FALSE)
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()", visible=FALSE)
pt$defineCalculation(calculationName="WeekendTrainsPercentage",
                     type="calculation", basedOn=c("WeekendTrains", "TotalTrains"),
                     format="%.1f %%",
                     calculationExpression="values$WeekendTrains/values$TotalTrains*100")
pt$renderPivot()

See the Appendix: Calculations vignette for many more examples.

% of Totals, Cumulative Sums, Running Differences, Rolling Averages, Ratios/Multiples

Changing the filter criteria used in a cell enables many additional types of calculations. See the Appendix: Calculations vignette for examples.

Example: % of row total:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="CountTrains", summariseExpression="n()", 
                     caption="Count", visible=FALSE)
filterOverrides <- PivotFilterOverrides$new(pt, keepOnlyFiltersFor="TOC")
pt$defineCalculation(calculationName="TOCTotalTrains", filters=filterOverrides, 
                     summariseExpression="n()", caption="TOC Total", visible=FALSE)
pt$defineCalculation(calculationName="PercentageOfTOCTrains", type="calculation", 
                     basedOn=c("CountTrains", "TOCTotalTrains"),
                     calculationExpression="values$CountTrains/values$TOCTotalTrains*100", 
                     format="%.1f %%", caption="% of TOC")
pt$renderPivot()

See the Appendix: Calculations vignette for an explanation of the above.

Formatting calculated values

Each cell in a pivot table has two values: A rawValue that is the result of the calculation in the cell. The rawValue is typically the same data type as the variable the calculation is based on, e.g. a sum() of numerical values will result in a numerical value. The formattedValue is the value that is displayed in the pivot table. The data type of the formattedValue is typically character.

The formatting of calculation results is specified by setting the format parameter when calling the defineCalculation function.

A number of different approaches to formatting are supported:

  • If format is a text value, then pivottabler invokes base::sprintf() with the specified format.
  • If format is a list, then pivottabler invokes base::format(), where the elements in the list become arguments in the function call.
  • If format is an R function, then this is invoked for each value.
  • If format is not specified, then base::as.character() is invoked to provide a default formatted value.

The above are the same approaches used when formatting data groups.

Some formatting behaviour depends on the data type of the value being formatted. For details see the Appendix: Details vignette.

Formatting examples with base::sprintf() and base::format()

The example below shows two different ways of formatting a value to 2dp. The “Mean Arr. Delay 1” column below is formatted using base::sprintf("%.2f", x). The “Mean Arr. Delay 2” column below is formatted using base::format(x, digits=2, nsmall=2).

library(pivottabler)
library(dplyr)
library(lubridate)

# derive some additional data
trains <- mutate(bhmtrains,
                 ArrivalDelta=difftime(ActualArrival, GbttArrival, units="mins"),
                 ArrivalDelay=ifelse(ArrivalDelta<0, 0, ArrivalDelta))

# create the pivot table
pt <- PivotTable$new()
pt$addData(trains)
pt$addRowDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="TotalTrains", caption="Total Trains", 
                     summariseExpression="n()")
pt$defineCalculation(calculationName="MeanArrivalDelay1", caption="Mean Arr. Delay 1", 
                     summariseExpression="mean(ArrivalDelay, na.rm=TRUE)", 
                     format="%.2f")
pt$defineCalculation(calculationName="MeanArrivalDelay2", caption="Mean Arr. Delay 2", 
                     summariseExpression="mean(ArrivalDelay, na.rm=TRUE)", 
                     format=list(digits=2, nsmall=2))
pt$renderPivot()

In some countries around the world, the thousand separator character is “.” and the decimal separator character is “,” (i.e. the opposite to countries such as the UK and USA). This can be accomplished in pivottabler using:

library(pivottabler)

pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory", totalCaption="All Categories")
pt$addRowDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="Value1", caption="Value 1", 
                     summariseExpression="mean(SchedSpeedMPH, na.rm=TRUE)*33.33333",
                     format=list(digits=1, nsmall=0, big.mark=".", decimal.mark=","))
pt$defineCalculation(calculationName="Value2", caption="Value 2", 
                     summariseExpression="sd(SchedSpeedMPH,na.rm=TRUE)*333.33333",
                     format=list(digits=1, nsmall=1, big.mark=".", decimal.mark=","))
pt$renderPivot()

Formatting examples with custom R function

Using a custom R format function allows bespoke formatting logic to be used. In the following (contrived) example, “Mean Arr. Delay 2” includes a descriptive prefix that shows whether a number is less than, roughly equal to or greater than 3.

library(pivottabler)
library(dplyr)
library(lubridate)

# derive some additional data
trains <- mutate(bhmtrains,
                 ArrivalDelta=difftime(ActualArrival, GbttArrival, units="mins"),
                 ArrivalDelay=ifelse(ArrivalDelta<0, 0, ArrivalDelta))

# custom format function
fmtAddComment <- function(x) {
  formattedNumber <- sprintf("%.1f", x)
  comment <- "-"
  if (x < 2.95) comment <- "Below 3:  "
  else if ((2.95 <= x) && (x < 3.05)) comment <- "Equals 3:  "
  else if (x >= 3.05) comment <- "Over 3:  "
  return(paste0(comment, " ", formattedNumber))
}

# create the pivot table
pt <- PivotTable$new()
pt$addData(trains)
pt$addRowDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="TotalTrains", caption="Total Trains", 
                     summariseExpression="n()")
pt$defineCalculation(calculationName="MeanArrivalDelay1", 
                     caption="Mean Arr. Delay 1", 
                     summariseExpression="mean(ArrivalDelay, na.rm=TRUE)", 
                     format="%.1f")
pt$defineCalculation(calculationName="MeanArrivalDelay2", 
                     caption="Mean Arr. Delay 2", 
                     summariseExpression="mean(ArrivalDelay, na.rm=TRUE)", 
                     format=fmtAddComment)
pt$renderPivot()

Using the fmtFuncArgs argument it is also possible to pass additional arguments to a custom R function used for formatting. When passing multiple arguments to the custom R function, the cell value is always passed to the custom function as x. In the example below, the number of decimal places is specified using the fmtFuncArgs argument:

library(pivottabler)
library(dplyr)
library(lubridate)

# derive some additional data
trains <- mutate(bhmtrains,
                 ArrivalDelta=difftime(ActualArrival, GbttArrival, units="mins"),
                 ArrivalDelay=ifelse(ArrivalDelta<0, 0, ArrivalDelta))

# custom format function
fmtNumDP <- function(x, numDP) {
  formatCode <- paste0("%.", numDP, "f")
  formattedNumber <- sprintf(formatCode, x)
  return(formattedNumber)
}

# create the pivot table
pt <- PivotTable$new()
pt$addData(trains)
pt$addRowDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="TotalTrains", caption="Total Trains", 
                     summariseExpression="n()")

# define calculations
# note the use of the same custom format function (fmtNumDP) 
# but specifying different decimal places
pt$defineCalculation(calculationName="MeanArrivalDelay1", caption="Mean Arr. Delay 1", 
                     summariseExpression="mean(ArrivalDelay, na.rm=TRUE)", 
                     format=fmtNumDP, fmtFuncArgs=list(numDP=1))
pt$defineCalculation(calculationName="MeanArrivalDelay2", caption="Mean Arr. Delay 2", 
                     summariseExpression="mean(ArrivalDelay, na.rm=TRUE)", 
                     format=fmtNumDP, fmtFuncArgs=list(numDP=2))
pt$renderPivot()

Empty cells

By default, where no data exists (for a particular combination of row and column headers) pivottabler will leave the pivot table cell empty. Sometimes it is desirable to display a value in these cells. This can be specified in two ways in the defineCalculation() function - either by specifying a value for either the noDataValue or noDataCaption arguments. The differences between these two options are as follows:

Comparison noDataValue argument noDataCaption argument
Allowed Data Type(s) integer or numeric character
format argument applies Yes (will be formatted) No (will be displayed as-is)
Will be used in other calculations Yes No

If the requirement is only to display a different value when there is no data, then noDataCaption is the right choice. Both approaches are demonstrated below, where the Virgin Trains, Ordinary Passenger cell has no data, so the empty cell value/caption is shown.

noDataValue Example 

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()", noDataValue=0)
pt$renderPivot()

noDataCaption Example 

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()", noDataCaption="-")
pt$renderPivot()

Using multiple data frames in the same pivot table

A pivot table can display data from multiple data frames. The following summarises the possible functionality:

  • A pivot table can contain multiple calculations. Many of the examples above in this vignette illustrate this.
  • Each calculation must be based on one data frame. This is specified as part of the arguments to the defineCalculation() function1.
  • Defining multiple calculations allows data from multiple data frames to be displayed in the pivot table.

Important: When adding multiple data frames to a pivot table, the data frame columns used for the data groups (i.e. row/column headings) must be conformed, i.e.:

  • The columns from the data frames placed on the row/column headings in the pivot table must be present in all of the data frames added to the pivot table.
  • Those columns must have the same names in all of the data frames added to the pivot table.
  • The data values used in those columns should be consistent (e.g. “England” must be “England” in all data frames added to the pivot table, not “England” in one data frame and “Eng”, “ENGLAND”, etc. in other data frames).

It is also worth noting that only the first data frame added to the pivot table is used when generating the row/column headings.

The example below illustrates using two data frames with a single pivot table:

library(pivottabler)
library(dplyr)

# derive some additional data
trains <- mutate(bhmtrains, 
  ArrivalDelta=difftime(ActualArrival, GbttArrival, units="mins"),
  ArrivalDelay=ifelse(ArrivalDelta<0, 0, ArrivalDelta),
  DelayedByMoreThan5Minutes=ifelse(ArrivalDelay>5,1,0)) %>%
  select(TrainCategory, TOC, DelayedByMoreThan5Minutes) 
# in this example, bhmtraindisruption is joined to bhmtrains
# so that the TrainCategory and TOC columns are present in both
# data frames added to the pivot table
cancellations <- bhmtraindisruption %>%
  inner_join(bhmtrains, by="ServiceId") %>%
  mutate(CancelledInBirmingham=ifelse(LastCancellationLocation=="BHM",1,0)) %>%
  select(TrainCategory, TOC, CancelledInBirmingham)

# create the pivot table
pt <- PivotTable$new()
pt$addData(trains, "trains")
pt$addData(cancellations, "cancellations")
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="DelayedTrains", dataName="trains", 
                     caption="Delayed", 
                     summariseExpression="sum(DelayedByMoreThan5Minutes, na.rm=TRUE)")
pt$defineCalculation(calculationName="CancelledTrains", dataName="cancellations", 
                     caption="Cancelled", 
                     summariseExpression="sum(CancelledInBirmingham, na.rm=TRUE)")
pt$renderPivot()

In the example above, the number of trains more than five minutes late is calculated from the trains data frame and the number of trains cancelled at Birmingham New Street is calculated from the cancellations data frame.

Advanced Calculation Methods

The following two methods of calculating cell values are more advanced and less commonly needed.

Calculation Method 3: Custom calculation functions

A custom calculation function allows more complex calculation logic to be used. Such a function is invoked once for each cell in the body of the pivot table. Custom calculation functions always have the same arguments defined:

  • pivotCalculator is a helper object that offers various methods to assist in performing calculations,
  • netFilters contains the definitions of the filter criteria coming from the row and column headers in the pivot table,
  • calcFuncArgs is a list that specifies any additional arguments that need to be passed to the custom calculation function,
  • format provides the formatting definition - this is the same value specified in the defineCalculation() call,
  • fmtFuncArgs is a list that specifies any additional arguments that need to be passed to a custom format function (if used),
  • baseValues provides access to the results of other calculations in the calculation group,
  • cell provides access to more details about the individual cell that is being calculated.
    • The cell argument is provided to support more advanced scenarios and is not explained in detail here.

For example, if we wish to examine the worst single day performance, we need to:

  1. For each date, calculate the percentage of trains more than five minutes late,
  2. Sort this list (of dates and percentages) into descending order (by percentage of trains more than five minutes late),
  3. Display the top percentage value from this list.
library(pivottabler)
library(dplyr)
library(lubridate)

# derive some additional data
trains <- mutate(bhmtrains,
   GbttDateTime=if_else(is.na(GbttArrival), GbttDeparture, GbttArrival),
   GbttDate=make_date(year=year(GbttDateTime), month=month(GbttDateTime), day=day(GbttDateTime)),
   GbttMonth=make_date(year=year(GbttDateTime), month=month(GbttDateTime), day=1),
   ArrivalDelta=difftime(ActualArrival, GbttArrival, units="mins"),
   ArrivalDelay=ifelse(ArrivalDelta<0, 0, ArrivalDelta),
   DelayedByMoreThan5Minutes=ifelse(ArrivalDelay>5,1,0))

# custom calculation function
getWorstSingleDayPerformance <- function(pivotCalculator, netFilters, calcFuncArgs, 
                                         format, fmtFuncArgs, baseValues, cell) {
  # get the data frame
  trains <- pivotCalculator$getDataFrame("trains")
  # apply the TOC and month filters coming from the headers in the pivot table
  filteredTrains <- pivotCalculator$getFilteredDataFrame(trains, netFilters)
  # calculate the percentage of trains more than five minutes late by date
  dateSummary <- filteredTrains %>%
    group_by(GbttDate) %>%
    summarise(DelayedPercent = sum(DelayedByMoreThan5Minutes, na.rm=TRUE) / n() * 100) %>%
    arrange(desc(DelayedPercent))
  # top value
  tv <- dateSummary$DelayedPercent[1]
  # build the return value
  value <- list()
  value$rawValue <- tv
  value$formattedValue <- pivotCalculator$formatValue(tv, format=format)
  return(value)
}

# create the pivot table
pt <- PivotTable$new()
pt$addData(trains, "trains")
pt$addColumnDataGroups("GbttMonth", dataFormat=list(format="%B %Y"))
pt$addRowDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="WorstSingleDayDelay", format="%.1f %%",
                     type="function", calculationFunction=getWorstSingleDayPerformance)
pt$renderPivot()

The return value from the custom function must be a list containing the raw result value (i.e. unformatted, that is either integer or numeric data type) and a formatted value (that is character data type).

Using a custom calculation function also enables additional possibilities, e.g. including additional information in the formatted value, in this case the date of the worst single day performance (where the code changes compared to the example above are highlighted):

library(pivottabler)
library(dplyr)
library(lubridate)

# derive some additional data
trains <- mutate(bhmtrains,
   GbttDateTime=if_else(is.na(GbttArrival), GbttDeparture, GbttArrival),
   GbttDate=make_date(year=year(GbttDateTime), month=month(GbttDateTime), 
                      day=day(GbttDateTime)),
   GbttMonth=make_date(year=year(GbttDateTime), month=month(GbttDateTime), day=1),
   ArrivalDelta=difftime(ActualArrival, GbttArrival, units="mins"),
   ArrivalDelay=ifelse(ArrivalDelta<0, 0, ArrivalDelta),
   DelayedByMoreThan5Minutes=ifelse(ArrivalDelay>5,1,0))

# custom calculation function
getWorstSingleDayPerformance <- function(pivotCalculator, netFilters, calcFuncArgs, 
                                         format, fmtFuncArgs, baseValues, cell) {
  # get the data frame
  trains <- pivotCalculator$getDataFrame("trains")
  # apply the TOC and month filters coming from the headers in the pivot table
  filteredTrains <- pivotCalculator$getFilteredDataFrame(trains, netFilters)
  # calculate the percentage of trains more than five minutes late by date
  dateSummary <- filteredTrains %>%
    group_by(GbttDate) %>%
    summarise(DelayedPercent = sum(DelayedByMoreThan5Minutes, na.rm=TRUE) / n() * 100) %>%
    arrange(desc(DelayedPercent))
  # top value
  tv <- dateSummary$DelayedPercent[1]
  date <- dateSummary$GbttDate[1]             #     <<  CODE CHANGE  <<
  # build the return value
  value <- list()
  value$rawValue <- tv
  value$formattedValue <- paste0(format(      #     <<  CODE CHANGE (AND BELOW)  <<
    date, format="%a %d"), ":  ", pivotCalculator$formatValue(tv, format=format))
  return(value)
}

# create the pivot table
pt <- PivotTable$new()
pt$addData(trains, "trains")
pt$addColumnDataGroups("GbttMonth", dataFormat=list(format="%B %Y"))
pt$addRowDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="WorstSingleDayDelay", format="%.1f %%",
                     type="function", calculationFunction=getWorstSingleDayPerformance)
pt$renderPivot()

Including two values in each cell somewhat reduces the readability however.

It is possible to pass additional arguments as a list to a custom calculation function, as illustrated in the trivial example below.

library(pivottabler)

# custom calculation function
calcFunction <- function(pivotCalculator, netFilters, calcFuncArgs, 
                         format, fmtFuncArgs, baseValues, cell) {

  # build the return value
  value <- list()
  value$rawValue <- calcFuncArgs$result
  value$formattedValue <- pivotCalculator$formatValue(calcFuncArgs$result, format=format)
  return(value)
}

# create the pivot table
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="calcA", caption="A", type="function", 
                     calculationFunction=calcFunction, calcFuncArgs=list(result=1))
pt$defineCalculation(calculationName="calcB", caption="B", type="function", 
                     calculationFunction=calcFunction, calcFuncArgs=list(result=2))
pt$renderPivot()

This is useful as it allows the logic in more complex custom calculation functions to be reused to create variations of the same function - without needing to define the entire function again. For example, taking the example above that calculates the worst single day performance, it is possible to split the two values in a single cell into two cells.

library(pivottabler)
library(dplyr)
library(lubridate)

# derive some additional data
trains <- mutate(bhmtrains,
                 GbttDateTime=if_else(is.na(GbttArrival), GbttDeparture, GbttArrival),
                 GbttDate=make_date(year=year(GbttDateTime), month=month(GbttDateTime), day=day(GbttDateTime)),
                 GbttMonth=make_date(year=year(GbttDateTime), month=month(GbttDateTime), day=1),
                 ArrivalDelta=difftime(ActualArrival, GbttArrival, units="mins"),
                 ArrivalDelay=ifelse(ArrivalDelta<0, 0, ArrivalDelta),
                 DelayedByMoreThan5Minutes=ifelse(ArrivalDelay>5,1,0))

# custom calculation function
getWorstSingleDayPerformance <- function(pivotCalculator, netFilters, calcFuncArgs, 
                                         format, fmtFuncArgs, baseValues, cell) {
  # get the data frame
  trains <- pivotCalculator$getDataFrame("trains")
  # apply the TOC and month filters coming from the headers in the pivot table
  filteredTrains <- pivotCalculator$getFilteredDataFrame(trains, netFilters)
  # calculate the percentage of trains more than five minutes late by date
  dateSummary <- filteredTrains %>%
    group_by(GbttDate) %>%
    summarise(DelayedPercent = sum(DelayedByMoreThan5Minutes, na.rm=TRUE) / n() * 100) %>%
    arrange(desc(DelayedPercent))
  # top value
  tv <- dateSummary$DelayedPercent[1]
  date <- dateSummary$GbttDate[1] 
  if(calcFuncArgs$output=="day") {  #     <<  CODE CHANGES HERE  << 
    # build the return value
    value <- list()
    value$rawValue <- date
    value$formattedValue <- format(date, format="%a %d")
  }
  else if(calcFuncArgs$output=="performance") {  #     <<  CODE CHANGES HERE  << 
    # build the return value
    value <- list()
    value$rawValue <- tv
    value$formattedValue <- pivotCalculator$formatValue(tv, format=format)
  }
  return(value)
}

# create the pivot table
pt <- PivotTable$new()
pt$addData(trains, "trains")
pt$addColumnDataGroups("GbttMonth", dataFormat=list(format="%B %Y"))
pt$addRowDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="WorstSingleDay", caption="Day", 
                     format="%.1f %%", type="function", 
                     calculationFunction=getWorstSingleDayPerformance,
                     calcFuncArgs=list(output="day"))
pt$defineCalculation(calculationName="WorstSingleDayPerf", caption="Perf", 
                     format="%.1f %%", type="function", 
                     calculationFunction=getWorstSingleDayPerformance,
                     calcFuncArgs=list(output="performance"))
pt$renderPivot()

Calculation Method 4: Showing a value (no calculation)

With this approach, the pivot table performs little or no calculations. The values to display are predominantly calculated in R code before the pivot table is created. This pivot table is used primarily as a visualisation mechanism.

Returning to the original simple example of the number of trains operated by each train operating company:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$renderPivot()

In the example above, pivottabler calculated the values in each pivot table cell. We can alternatively calculate the values explicitly in R code and instead just use the pivot table to display them:

library(pivottabler)

# perform the aggregation in R code explicitly
trains <- bhmtrains %>%
  group_by(TrainCategory, TOC) %>%
  summarise(NumberOfTrains=n()) %>%
  ungroup()

# a sample of the aggregated data
head(trains)
## # A tibble: 6 × 3
##   TrainCategory      TOC                 NumberOfTrains
##   <fct>              <fct>                        <int>
## 1 Express Passenger  Arriva Trains Wales           3079
## 2 Express Passenger  CrossCountry                 22865
## 3 Express Passenger  London Midland               14487
## 4 Express Passenger  Virgin Trains                 8594
## 5 Ordinary Passenger Arriva Trains Wales            830
## 6 Ordinary Passenger CrossCountry                    63
# display this pre-calculated data
pt <- PivotTable$new()
pt$addData(trains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", type="value", valueName="NumberOfTrains")
pt$renderPivot()

In the simple version shown above, no totals are displayed. This is because the data frame contains only line-level data, i.e. no aggregated data.

Three workarounds are possible:

  • The total rows and/or columns can be hidden in the pivot table.
  • The totals can be pre-calculated and added to the pivot table.
  • A summarise expression can be specified to allow the pivot table to calculate the totals.

Each these examples are presented below.

Hiding the totals 

library(pivottabler)

# perform the aggregation in R code explicitly
trains <- bhmtrains %>%
  group_by(TrainCategory, TOC) %>%
  summarise(NumberOfTrains=n()) %>%
  ungroup()

# display this pre-calculated data
pt <- PivotTable$new()
pt$addData(trains)
pt$addColumnDataGroups("TrainCategory", addTotal=FALSE)   #  <<  *** CODE CHANGE ***  <<
pt$addRowDataGroups("TOC", addTotal=FALSE)                #  <<  *** CODE CHANGE ***  <<
pt$defineCalculation(calculationName="TotalTrains", type="value", valueName="NumberOfTrains")
pt$renderPivot()

Pre-calculating the totals outside the pivot table

The totals are calculated in separate data frames then added to the pivot table using pt$addTotalData().

The variableNames must be specified to inform the pivot table about which total cells each totals data frame relates to. If a given cell is a subtotal that relates to multiple variables, specify the variable names using c(var1, var2, var3, ...).

library(dplyr)
library(pivottabler)

# perform the aggregation in R code explicitly
trains <- bhmtrains %>%
  group_by(TrainCategory, TOC) %>%
  summarise(NumberOfTrains=n()) %>%
  ungroup()

# calculate the totals/aggregate values
trainsTrainCat <- bhmtrains %>%
  group_by(TrainCategory) %>%
  summarise(NumberOfTrains=n()) %>%
  ungroup()

trainsTOC <- bhmtrains %>%
  group_by(TOC) %>%
  summarise(NumberOfTrains=n()) %>%
  ungroup()

trainsTotal <- bhmtrains %>%
  summarise(NumberOfTrains=n())

# display this pre-calculated data
pt <- PivotTable$new()
pt$addData(trains)
pt$addTotalData(trainsTrainCat, variableNames="TrainCategory")
pt$addTotalData(trainsTOC, variableNames="TOC")
pt$addTotalData(trainsTotal, variableNames=NULL)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", type="value", valueName="NumberOfTrains")
pt$renderPivot()

Pivot table calculating the totals 

library(pivottabler)

# perform the aggregation in R code explicitly
trains <- bhmtrains %>%
  group_by(TrainCategory, TOC) %>%
  summarise(NumberOfTrains=n()) %>%
  ungroup()

# display this pre-calculated data
pt <- PivotTable$new()
pt$addData(trains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains",  # <<  *** CODE CHANGE (AND BELOW) *** <<
                     type="value", valueName="NumberOfTrains", 
                     summariseExpression="sum(NumberOfTrains)")
pt$renderPivot()

Performance considerations

The pivottabler package supports two different evaluation modes for computing cell values: batch and sequential. The batch evaluation mode offers much higher performance, especially for large pivot tables.

The pivottabler package is also able to use two different packages when carrying out summarising cell calculations (method 1 calculations as described above in this vignette): dplyr and data.table. data.table offers slightly higher performance when used with large data frames (over ten million rows) with batch evaluation mode, though has some quirks/odd behaviours when aggregating over columns that are also used in the row/column headings of the pivot table.

Please see the Performance for more information about pivottabler performance and data.table.

Further Reading

The full set of vignettes is:

  1. Introduction
  2. Data Groups
  3. Calculations
  4. Regular Layout
  5. Outputs
  6. Latex Output
  7. Styling
  8. Finding and Formatting
  9. Cell Context
  10. Navigating a Pivot Table
  11. Irregular Layout
  12. Performance
  13. Excel Export
  14. Shiny
  15. Appendix: Details
  16. Appendix: Calculations
  17. Appendix: Class Overview

  1. If the pivot table contains only one data frame, then specifying the data frame when calling defineCalculation() is not necessary.↩︎